Image processing method and device

ABSTRACT

An image processing method includes obtaining a first quantity of to-be-analyzed images and performing fusion and enhancement processing on the first quantity of to-be-analyzed images through an image analysis model to obtain a first target image. Each to-be-analyzed image corresponds to a different target modality of a target imaging object. The first target image is used to enhance display of a distribution area of an analysis object of the first quantity of to-be-analyzed images. The analysis object belongs to the imaging object. The image analysis model is obtained by training a second quantity of sample images corresponding to different sample modalities. The first quantity is less than or equal to the second quantity. The target modality belongs to the sample modalities.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.202111154668.8, filed on Sep. 29, 2021, the entire content of which isincorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to the image processingtechnology field and, more particularly, to an image processing methodand device.

BACKGROUND

With the rapid development of computer graphics and image processingtechnology, computers have been widely used in the medical field.Through analysis processing of various diagnostic images by computers,accurate diagnosis efficiency of medical staff is improved. Currently,commonly used computer-aided diagnosis systems usually performcorresponding enhancement processing on magnetic resonance imaging (MRI)or computed tomography (CT) images so that doctors can quickly determinelocations of lesions. For example, when a current computer-aideddiagnosis system determines a hepatic tumor, a dual-modal scheme isusually used to realize the diagnosis. That is, an enhanced CT imagewhen contrast agent is in the hepatic vein and a CT image when contrastagent is in the hepatic artery are collected for a period of time foranalysis. Since the enhanced CT image when the contrast agent is in thehepatic vein and the CT image when the contrast agent is in the hepaticartery can complement information with each other well, which helps tobetter diagnose the hepatic tumor.

However, if only one modal image information or more than 2 types ofmodal image information are available, the above-mentioned dual-modalsolution cannot be used to perform image processing. A new correspondingimage processing method needs to be redeveloped, which causes thecurrent image processing method to be less efficient.

SUMMARY

Embodiments of the present disclosure provide an image processingmethod. The method includes obtaining a first quantity of to-be-analyzedimages that need to be analyzed and performing fusion and enhancementprocessing on the first quantity of to-be-analyzed images that need tobe analyzed through an image analysis model to obtain a first targetimage. Each to-be-analyzed image corresponds to a different targetmodality of a target imaging object. The first target image is used toenhance display of a distribution area of an analysis object of thefirst quantity of to-be-analyzed images. The analysis object belongs tothe imaging object. The image analysis model is obtained by training asecond quantity of sample images corresponding to different samplemodalities. The first quantity is less than or equal to the secondquantity. The target modality belongs to the sample modalities.

Embodiments of the present disclosure provide an image processingdevice, including an acquisition unit and a model processing unit. Theacquisition unit is configured to obtain a first quantity ofto-be-analyzed images. Each to-be-analyzed image corresponds to adifferent target modality of a target imaging object. The modelprocessing unit is configured to perform fusion and enhancementprocessing on the first quantity of to-be-analyzed images through animage analysis model to obtain a first target image. The first targetimage is used to enhance display of a distribution area of an analysisobject of the first quantity of to-be-analyzed images. The analysisobject belongs to the imaging object. The image analysis model isobtained by training a second quantity of sample images corresponding todifferent sample modalities. The first quantity is less than or equal tothe second quantity. The target modality belongs to the samplemodalities.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings are merely examples for illustrative purposesaccording to various disclosed embodiments and are not intended to limitthe scope of the present disclosure.

FIG. 1 illustrates a schematic flowchart of an image processing methodaccording to some embodiments of the present disclosure.

FIG. 2 illustrates a schematic flowchart of another image processingmethod according to some embodiments of the present disclosure.

FIG. 3 illustrates a schematic flowchart of still another imageprocessing method according to some embodiments of the presentdisclosure.

FIG. 4 illustrates a schematic flowchart of still another imageprocessing method according to some embodiments of the presentdisclosure.

FIG. 5 illustrates a schematic structural diagram of a model accordingto some embodiments of the present disclosure.

FIG. 6 illustrates a schematic diagram of an arterial phase liver imageaccording to some embodiments of the present disclosure.

FIG. 7 illustrates a schematic diagram of a venous phase liver imageaccording to some embodiments of the present disclosure.

FIG. 8 illustrates a schematic diagram of a first target image accordingto some embodiments of the present disclosure.

FIG. 9 illustrates a schematic structural diagram of an image processingdevice according to some embodiments of the present disclosure.

FIG. 10 illustrates a schematic structural diagram of an electronicapparatus according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical solutions of embodiments of the present disclosure aredescribed in detail below with reference to the accompanying drawings ofembodiments of the present disclosure.

Embodiments of the present disclosure provide an image processingmethod. Referring to FIG. 1 , the method is applied to an electronicdevice. The method includes the following steps.

At 101, a first quantity of images that need to be analyzed (i.e.,to-be-analyzed images) are obtained.

Each image that needs to be analyzed corresponds to a different targetmodel of a target imaging object.

In embodiments of the present disclosure, the electronic device is adevice with computation and analysis capabilities, for example, acomputer device, a server device, a smart mobile device, etc. The firstquantity of images that need to be analyzed may include images that areobtained by using different imaging methods for the same target objectand need to be analyzed. That is, one image that needs to be analyzedcorresponds to one modality. As such, the first quantity of images thatneed to be analyzed correspond to a first quantity of modalities. Theimage that needs to be analyzed to may be an image that needs to beanalyzed in various fields, for example, an image that needs to beenhanced and displayed in an area where an analysis object is located inthe medical field.

In some embodiments, an image of a patient liver may be collected in acomputed tomography (CT) method to obtain a venous phase image ofcontrast agent in the vein and an arterial phase image of contrast agentin the artery. The venous phase image and the arterial phase image maybe called images of two modalities. The images of the liver of the samepatient may also be collected in the CT method.

At 102, fusion and enhancement processing is performed on the firstquantity of images that need to be analyzed by using an image analysismodel to obtain a first target image.

The first target image may be used to enhance display of a distributionarea of the analysis object in the first quantity of images that need tobe analyzed. The analysis object may be an imaging object. The imageanalysis model may be obtained by training a second quantity of sampleimages corresponding to different sample modalities. The first quantitymay be less than or equal to the second quantity. The target modalitymay be a sample modality.

In embodiments of the present disclosure, the image analysis model maybe an analysis model obtained by training a large quantity of sampleimages, including a second quantity of different sample modalities.During an analysis process, input objects with the most quantity of theimage analysis model may be the images that need to be analyzed of thesecond quantity of different sample modalities, and at least images thatneed to be analyzed of one sample modality. Since the image analysismodel is obtained by training according to a large number of sampleimages, including the second quantity of different sample modalities,parameters corresponding to each sample modality in the image analysismodel may be determined by other sample modalities. Thus, the imageanalysis model may supplement features of other sample modalities. Assuch, when the first quantity of images that need to be analyzed inputis less than the second quantity, the image analysis model can alsosupplement feature information of missing sample modalities. The imageanalysis modal may perform the fusion and enhancement processing on thefirst quantity of images that need to be analyzed to obtain the firsttarget image.

Embodiments of the present disclosure provide an image processingmethod. After obtaining the first quantity of images that need to beanalyzed, the method further includes performing the fusion andenhancement processing on the first quantity of images that need to beanalyzed through the image analysis model to obtain the first targetimage. As such, since the first quantity may be less than the secondquantity of all inputs of the image analysis model, the image analysismodel may perform the fusion and enhancement processing on the firstquantity of images that need to be analyzed of the missing modalities toobtain the first target for the analysis object. The problem that thecurrent image processing method cannot realize image analysis when themodality is missing may be solved. Thus, when the modality is missing,the unified image processing method may still be used for analysis,which improves the processing efficiency of the graphic processingmethod.

Based on the above embodiments, embodiments of the present disclosureprovide an image processing method. The method is applied to anelectronic device. The method includes the following steps.

At 201, a first quantity of images that need to be analyzed areobtained.

Each image that needs to be analyzed corresponds to a different targetmodality of a target imaging object.

In embodiments of the present disclosure, for example, the images thatneed to be analyzed may include a hepatic CT image collected in a CTmanner. A first quantity of hepatic CT images that need to be analyzedmay be currently collected to obtain the first quantity of images thatneed to be analyzed.

At 202, fusion and enhancement processing is performed on the firstquantity of images that need to be analyzed by using the image analysismodel to obtain a first target image.

The first target image may be used to enhance the display of adistribution area of the analysis object in the first quantity of imagesthat need to be analyzed. The analysis object may be an imaging object.The image analysis model may be obtained by training a second quantityof sample images corresponding to different sample modalities. The firstquantity may be less than or equal to the second quantity. The targetmodality may be a sample modality.

In embodiments of the present disclosure, the corresponding imageanalysis model may include a model that meets the requirements and isobtained by performing model training with a large quantity of hepaticCT images, including the second quantity of hepatic CT images. Assumingthat the second quantity is 2, the corresponding two sample modalitiesmay be the arterial phase hepatic CT image modality and the venous phasehepatic CT image modality, respectively. Thus, the first quantity may be1 or 2, which is usually determined by a quantity of images of acollected sample modality.

Both the first quantity and the second quantity may be determined byactual application scenes, which are not limited here.

Based on the above embodiments, in other embodiments of the presentdisclosure, referring to FIG. 2 , step 202 a may include, if the firstquantity is 1, processing the corresponding images that need to beanalyzed based on a feature extraction sub-model corresponding to thefirst quantity of images that need to be analyzed of different targetmodalities in the image analysis model to obtain the first target image.

The feature extraction sub-model corresponding to each image that needsto be analyzed may represent a relationship between the images that needto be analyzed of the second quantity of different sample modalities.

In embodiments of the present disclosure, when the first quantity is 1,a feature extraction sub-model corresponding to a target modality of oneobtained image that needs to be analyzed may be determined from theimage analysis model. The determined and obtained feature extractionsub-model may be used to perform image feature extraction processing onthe one image that needs to be analyzed to obtain the first targetimage.

Since the image analysis model is obtained by performing model trainingaccording to a large quantity of sample images including the secondquantity of sample modalities, relevant parameters in the featureextraction sub-model of each sample modality may not only include samplemodal information of the sample modality but also learn sample modalinformation of other sample modalities. As such, when the image analysismodel is used to analyze the image that needs to be analyzed of a targetmodality, an effect when the images that need to be analyzed of thesecond quantity of sample modalities may be approximately achieved.

Based on the above embodiments, in other embodiments of the presentdisclosure, referring to FIG. 3 , step 202 may include steps 202 b to202 c.

At 202 b, if the first quantity is greater than or equal to 2 and lessthan or equal to the second quantity, the corresponding images that needto be analyzed are processed using the feature extraction sub-modelcorresponding to the first quantity of images that need to be analyzedof different target modalities in the image analysis model to obtain afirst quantity of reference images.

The feature extraction sub-model corresponding to each image that needsto be analyzed may represent a relationship between the images that needto be analyzed of the second quantity of different sample modalities.

In embodiments of the present disclosure, when the first quantity isgreater than or equal to 2 and less than or equal to the secondquantity, from the image analysis model, the feature extractionsub-model corresponding to the target modality of each image that needsto be analyzed may be determined. Then, the feature extraction sub-modelcorresponding to the target modality of each image that needs to beanalyzed may be used to perform image feature extraction processing onthe corresponding image that needs to be analyzed to obtain a featureimage corresponding to each image that needs to be analyzed, that is, areference image, to obtain the first quantity of reference images.

At 202 c, image processing is performed on the first quantity ofreference images using a modal perception sub-model to obtain the firsttarget image.

In embodiments of the present disclosure, after the first quantity ofreference images are obtained, the modality perception sub-model of theimage analysis model may be used to perform the image processing on thefirst quantity of reference images to realize feature enhancement andimage fusion processing to obtain the first target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, the modal perception sub-model may be used to perform imagefusion processing on reference images of at least two differentmodalities. In some other embodiments, the modal perception sub-modelmay be used to perform the image fusion processing after performing thefeature enhancement processing on the reference images of the at leasttwo different sample modalities.

In this embodiment of the present application, the modality perceptionsub-model in the image analysis model may directly perform image fusionprocessing on the first quantity of reference images, enhancerepresentation of commonalities in the reference images, and weakennon-commonalities to obtain the first target image.

The modality perception sub-model in the image analysis model mayfurther perform feature enhancement processing on the first quantity ofreference images, and then perform image fusion processing on the firstquantity of reference images after feature enhancement processing toobtain the first target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, the modality perception sub-model is used to perform theimage fusion processing after performing the feature enhancementprocessing on the reference images of the at least two different samplemodalities. Step 202 c may include steps a11 to a13.

At a11, the modal perception sub-model is used to perform the imagefusion processing on the first quantity of reference images to obtainthe target fusion image.

In embodiments of the present disclosure, the modal perception sub-modelmay be used to perform the image fusion processing on the first quantityof reference images extracted by the feature extraction sub-module toobtain the target fusion image.

At a12, the modal perception sub-model is used to determine a similaritycoefficient between each reference image and the target fusion image toobtain a first quantity of similarity coefficients.

In embodiments of the present disclosure, after obtaining the targetfusion image, the modal perception sub-model may be used to calculatethe similarity coefficient between each reference image and the targetfusion image to obtain the similarity coefficient corresponding to eachreference image to obtain the first quantity of similarity coefficients.The modal perception sub-model may be used to calculate the similaritycoefficient between each reference image and the target fusion imagethrough similarity coefficient calculation methods, such as histogrammatching method, hash algorithm, etc. The modal perception sub-model mayalso be used to calculate the similarity coefficient between eachreference image and the target fusion image by using some neural networkmodel algorithms.

When the modal perception sub-model uses the neural network modelalgorithm to calculate the similarity coefficient between each referenceimage and the target fusion image, the corresponding modal perceptionsub-model may be realized by at least two cascaded convolutional layers.In the at least two cascaded convolutional layers, an instancenormalization layer and a leak rectified linear unit layer may bearranged after each convolutional layer before the last convolutionallayer. When the modal perception sub-model is implemented by using twocascaded convolutional layers, a size of a convolution kernel of a firstconvolutional layer configured to process the first reference analysisimage may be, for example, 3*3*3. A size of the convolution kernel ofthe second convolution layer configured to process a processing resultof the leak rectified linear unit layer after a first convolution layermay be, for example, 1*1*1.

At a13, the modal perception sub-model is configured to perform featureenhancement processing on the first quantity of similarity coefficientsand the first quantity of reference images to obtain the first targetimage.

In embodiments of the present disclosure, after determining andobtaining the similarity coefficient of each reference image, the modalperception sub-model may perform multiplication on the similaritycoefficient of each reference image and each corresponding referenceimage. Thus, the modal perception sub-model may perform the featureenhancement processing on the corresponding reference image through eachsimilarity coefficient to obtain the first target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, step a13 may include steps a131 and a132.

At a131, the modal perception sub-model is configured to perform thefeature enhancement processing on the similarity coefficientcorresponding to each reference image to obtain a first quantity ofsub-feature images.

In embodiments of the present disclosure, the modal perception sub-modelmay be configured to calculate a product between each reference imageand the corresponding similarity coefficient. That is, the modalperception sub-model may be configured to perform weighting processingon each reference image to realize the feature enhancement processingfor each reference image and obtain a sub-feature image corresponding toeach reference image to obtain the first quantity of sub-feature images.

At a132, the modal perception sub-model is configured to perform imagefusion processing on the first quantity of sub-feature images to obtainthe first target image.

In embodiments of the present disclosure, the modal perception sub-modelmay be configured to perform superposition processing on the obtainedfirst quantity of sub-feature images. That is, the modal perceptionsub-model may be configured to realize the image fusion processing toobtain the first target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, as shown in FIG. 4 , before step 201, the electronic deviceis further configured to execute steps 203 to 205.

At 203, a third quantity of groups of sample images and a third quantityof marked positions of the analysis object of the third quantity ofgroups of sample images are obtained.

Each group of sample images may include a second quantity of sampleimages corresponding to different sample modalities.

In embodiments of the present disclosure, the third number may usuallybe a minimum number of samples required for model training. The markedposition is the position of the analysis object in the sample image.Since each group of sample images are obtained by imaging a same imagingobject in different sample modalities, in the same group of sampleimages, the analysis object may only have one marked position.

At 204, a image model that needs to be trained is determined.

At 205, model training is performed on the image model that needs to betrained using the third quantity of groups of sample images and thethird quantity of marked positions to obtain an image analysis model.

In embodiments of the present disclosure, the third quantity of groupsof sample images and the third quantity of marked positions may be usedto perform the model training on the image model that needs to betrained to adjust and modify weight coefficients in the image model thatneeds to be trained. Thus, a loss value between the model obtained afterthe training and the corresponding marked position may be smaller than apredetermined threshold. Then, the model obtained after the training maybe determined to be the image analysis model.

Steps 203 to 205 may also be implemented as an independent embodiment.That is, before step 201, steps 203 to 205 may be implemented as anindependent embodiment. In the embodiment, the method may includeperforming the model training on the image model that needs to betrained to obtain the image analysis model. Thus, the trained imageanalysis model may be directly called subsequently.

Based on the above embodiments, in other embodiments of the presentdisclosure, step 205 may include steps 205 a and 205 b.

At 205 a, the image model that needs to be trained is configured toperform fusion and enhancement processing on an area where the analysisobject is located in the third quantity of groups of sample images toobtain the third quantity of second target images.

In embodiments of the present disclosure, for a specific implementationprocess of step 205 a, reference may be made to steps 202 b and 202 c,steps a11 to a13, and steps a131 and a132, which is not described indetail here.

At 205 b, based on the third quantity of groups of sample images, thethird quantity of second target images, and the third quantity of markedpositions, model training is performed on the image model that needs tobe trained to obtain the image analysis model.

In embodiments of the present disclosure, the third quantity of groupsof sample images, the third quantity of second target images, and athird quantity of marked positions may be used to perform the modeltraining on the image model that needs to be trained to determine weightcoefficients in the expected model that needs to be trained to obtainthe image analysis model.

From the third quantity of groups of sample images, a group of sampleimages may be obtained in sequence to obtain the target group sampleimages. The image model that needs to be trained may be configured toperform fusion and enhancement processing on the target group sampleimages to obtain a second target image. Based on the target group sampleimages, the second target image corresponding to the target group sampleimages, and the marked positions corresponding to the target groupsample images, the corresponding loss value may be calculated. If theloss value is less than a predetermined loss threshold, the loss valuemay be transferred reversely in the image model that needs to be trainedto update the parameters in the image model that needs to be trained andupdate the image model that needs to be trained to a image model thatneeds to be trained after the parameters are updated. A next group ofsample images adjacent to the target group sample images may becontinued to be obtained from the third quantity of groups of sampleimages. The next group of sample images may be updated to be the targetgroup sample images. In some other embodiments, a group of sample imagesmay be obtained randomly from the third quantity of groups of sampleimages. The group of sample images may be updated to be the target groupsample images. The process may repeat so on until the calculated lossvalue is less than the predetermined loss threshold. Then, the imagemodel that needs to be trained corresponding to the predetermined lossthreshold is the image analysis model.

Based on the above embodiments, in other embodiments of the presentdisclosure, step 205 b may include steps b11 to b13.

At b11, a loss value between each sample image in the target groupsample images and a corresponding marked position is determined toobtain a first loss value corresponding to the target group sampleimages.

The first loss value corresponding to the target group sample images mayinclude a second quantity of loss values.

In embodiments of the present disclosure, a loss function may be used tocalculate the loss value between each sample image of the target groupsample images and the corresponding marked position to obtain the lossvalue of each sample image of the target group sample images. Thus, thefirst loss value corresponding to the target group sample images may beobtained.

At b12, the loss value between the second target image corresponding tothe target group sample images and the corresponding marked position isdetermined to obtain a second loss value corresponding to the targetgroup sample images.

In embodiments of the present disclosure, the loss function may be usedto calculate the loss value between the second target imagecorresponding to the target group sample images and the correspondingmarked position to obtain the second loss value corresponding to thetarget group sample images.

At b13, a first loss value corresponding to the target group sampleimages and a second loss value corresponding to the target group sampleimages are reversely transferred in the image model that needs to betrained to continuously train the parameters of the image model thatneeds to be trained to obtain the image analysis model.

In embodiments of the present disclosure, an accumulated sum of thefirst loss values corresponding to the target group sample images may bedetermined. A product of the accumulated sum and a predetermined weightcoefficient may be calculated. A sum of the product and the second lossvalue corresponding to the target group sample images may be calculated.Thus, the loss value corresponding to the target group sample images maybe obtained. If the loss value is less than or equal to thepredetermined loss threshold, the corresponding image model that needsto be trained may be determined to be the image analysis model. If theloss value is greater than the predetermined loss threshold, the lossvalue may be reversely transferred to the image model that needs to betrained to update the parameters of the image model that needs to betrained to obtain a first image analysis model. The image model thatneeds to be trained may be updated to the first image analysis model.Operations corresponding to step 205 a and steps b11 to b13 may berepeatedly performed until the image analysis model is obtained.

Based on the above embodiments, embodiments of the present disclosureprovide an implementation process of a method for performing imageprocessing on a hepatic CT scan image. Correspondingly, as shown in FIG.5 , embodiments of the present disclosure provide a model structure whena image model that needs to be trained is trained. The model structureincludes an arterial phase hepatic CT input node 31, a venous phasehepatic CT input node 32, an arterial phase feature extraction sub-model33, a venous phase feature extraction sub-model 34, a modal perceptionsub-model 35, a target image output node 36, and a loss calculation node37. The modal perception sub-model 35 includes a first image fusionmodule 351, an arterial phase similarity coefficient analysis module352, a venous phase similarity coefficient analysis module 353, and asecond image fusion module 354. The loss calculation node 37 includes anarterial phase loss value calculation module 371, a venous phase lossvalue calculation module 372, a joint loss value calculation module 373,and a comprehensive loss value calculation module 374.

In a model training phase, an example is taken to describe aninformation transfer process when a group of sample images, that is, thefirst arterial phase hepatic image and the first venous phase hepaticimage of the same case, may be used to perform model training. The firstarterial phase hepatic image may be input to the arterial phase hepaticCT input node 31. The first venous phase hepatic image may be input tothe venous phase hepatic CT input node 32. The arterial phase hepatic CTinput node 31 may be configured to send the first arterial phase hepaticimage to the arterial phase feature extraction sub-model 33. Thearterial phase feature extraction sub-model 33 may be configured toperform feature extraction on the first arterial phase hepatic image toobtain a first arterial phase reference image. Similarly, the venousphase feature extraction sub-model 34 may be configured to performfeature extraction on the first venous phase hepatic image to obtain afirst venous phase reference image.

The first image fusion module 351 may be configured to perform imagefusion processing on the first arterial phase reference image and thefirst venous phase reference image to obtain a first target fusionimage. The arterial phase similarity coefficient analysis module 352 maybe configured to perform similarity coefficient calculation on the firsttarget fusion image and the first arterial phase reference image toobtain a first arterial similarity coefficient. Similarly, the venousphase similarity coefficient analysis module 353 may be configured toperform similarity coefficient calculation on the first target fusionimage and the first venous phase reference image to obtain a firstvenous similarity coefficient. The second image fusion module 354 may beconfigured to perform feature enhancement processing on the firstarterial phase reference image by using the first arterial similaritycoefficient to obtain a first arterial sub-feature image. Similarly, thesecond image fusion module 354 may be configured to perform featureenhancement processing on the first venous phase reference image byusing the first venous similarity coefficient to obtain the first venoussub-feature image.

Then, the second image fusion module 354 may be configured to performimage fusion processing on the first arterial sub-feature image and thefirst venous sub-feature image to obtain the first target image. Thearterial phase loss value calculation module 371 may be configured tocalculate a first arterial phase loss value L1=L_(intra)(Y|X_(ap);W_(ap)) based on the first arterial phase hepatic image X_(ap) and ahepatic tumor marked position Y. W_(ap) is a corresponding parametercoefficient of the arterial feature extraction sub-model 33 and thearterial phase similarity coefficient analysis module 352 in the modelthat needs to be trained. Similarly, the venous phase loss valuecalculation module 372 may be configured to calculate a first venousloss value L2=L_(intra)(Y|X_(vp); W_(vp)) based on the first venousphase hepatic image X_(vp) and the hepatic tumor marked position Y.W_(vp) is the corresponding parameter coefficient of the venous phasefeature extraction sub-model 34 and the venous phase similaritycoefficient analysis module 353 in the model that needs to be trained.The joint loss value calculation module 373 may be configured tocalculate a first joint loss value L3=L_(joint)(Y|X;W), W={W_(ap),W_(vp)} based on a first target image X and the hepatic tumor markedposition Y. The comprehensive loss value module 374 may be configured tocalculate a final loss value through the following formula.

L=λ(L1+L2)+L3=λΣ_(i=ap,vp) L _(intra)(Y|X _(i) ;W _(i))+L_(joint)(Y|X;W)

If the final loss value is greater than the predetermined lossthreshold, the final loss value may be reversely transferred to themodel that needs to be trained shown in FIG. 5 . Parameters of thearterial phase feature extraction sub-model 33, the venous phase featureextraction sub-model 34, and the modal perception sub-model 35 may beupdated. Then, the updated model that needs to be trained shown in FIG.5 may repeat the above process until the trained final loss value isdetermined to be smaller than or equal to the predetermined lossthreshold. Then, the corresponding model that needs to be trained may bedetermined to be the image analysis model.

The arterial phase feature extraction sub-model 33 and the venous phasefeature extraction sub-model 34 may both be implemented by using a fullconvolution network (FCN).

A process of determining the similarity coefficient by the arterialphase similarity coefficient analysis module 352 and the venous phasesimilarity coefficient analysis module 353 may be represented by thefollowing formula:

A _(i)=δ(f _(a)([F _(dual) ;F _(i)];θ_(i))), i=ap,vp,

Where δ denotes a sigmoid function, θ denotes a parameter learned byf_(a), which consists of two cascaded convolution layers. A firstconvolution layer may include a 3×3×3 convolution kernel, and a secondconvolutional layer may include a 1×1×1 convolution kernel. Eachconvolution layer is followed by an instance normalization and a leakyrectified linear unit. F_(dual) denotes the first target fusion image,F_(ap) denotes a first arterial phase reference image, the F_(vp)denotes a first venous phase reference image, A_(ap) denotes the firstarterial phase similarity coefficient, and A_(vp) denotes the firstvenous phase similarity coefficient. A convolution operation may be usedto model a correlation between discriminative dual-modal information andeach modal feature.

An implementation process of the second image fusion module 354 may berepresented by the following formula:

Fatt=Σ_(i=ap,vp) A _(i) *F _(i) =A _(ap) *F _(ap) +A _(vp) *F _(vp),

where A_(ap) denotes the first arterial similarity coefficient, A_(vp)denotes the first venous similarity coefficient, F_(ap) denotes thefirst arterial phase reference image, and F_(vp) denotes the firstvenous phase reference image.

As such, the first image fusion module 351 may obtain the first targetfusion image through convolution. Although the first target fusion imageincludes the arterial information and venous information of the hepatictumor, the first target fusion image also inevitably introducesredundant noise of each sample modality when the hepatic tumor issegmented. In order to reduce the redundant noise, the venous phasesimilarity coefficient analysis module 353 and the arterial phasesimilarity coefficient analysis module 352 are provided to calculateinfluence of each sample modality through an attention mechanism. Thus,contribution of each sample modality may be measured adaptively andinterpreted visually. Further, in a process of calculating the finalloss value, the arterial phase loss value calculation module 371 mayencourage each branch to learn to distinguish specific arterial phasefeatures. The venous phase loss value calculation module 372 mayencourage each branch to learn to distinguish the specific venous phasefeatures. The joint loss value calculation module 373 may encourage eachbranch to learn from each other to maintain commonalities betweenhigh-level features to better combine multi-modal information. As such,the model training may be performed through the above loss valuedetermination method. Thus, a combination of cross entropy loss andslice loss may be used as a segmentation loss, which effectively reducesinfluence of an uneven distribution of tumor data.

Assuming that the image analysis model may be obtained after modeltraining is performed according to the image model that needs to betrained shown in FIG. 5 . The first quantity of images that need to beanalyzed may be 1. In some embodiments, when the to-be-analyzed image isthe to-be-analyzed second arterial phase hepatic image, correspondingly,the second arterial phase hepatic image may be input to the arterialphase feature extraction sub-model 33 through the arterial phase hepaticCT input node 31. The arterial phase feature extraction sub-model 33 maybe configured to perform feature extraction on the second arterial phasehepatic image to obtain the second arterial phase reference image. Sincea missing part exists in the venous phase hepatic image, the secondarterial phase reference image may be directly output from the targetimage output node 36. That is, the first target image corresponding tothe second arterial phase hepatic image may be obtained. When the imagethat needs to be analyzed is a to-be-analyzed second venous phasehepatic image, for the implementation process, reference may be made tothe implementation process of the image that needs to be analyzed beingthe to-be-analyzed second arterial phase hepatic image, which is not berepeated here.

When the first quantity is equal to the second quantity 2, and theimages that need to be analyzed include a third arterial phase hepaticimage as shown in FIG. 6 and a third venous phase hepatic image as shownin FIG. 7 , the third arterial phase hepatic image may be input to thearterial phase hepatic CT input node 31, and the third venous phasehepatic image may be input to the venous phase hepatic CT input node 32.The arterial phase hepatic CT input node 31 may be configured to sendthe third arterial phase hepatic image to the arterial phase featureextraction sub-model 33. The arterial phase feature extraction sub-model33 may be configured to perform feature extraction on the third arterialphase hepatic image to obtain a third arterial phase reference image.Similarly, the venous phase feature extraction sub-model 34 may performfeature extraction on the third venous phase hepatic image to obtain thethird venous phase reference image.

The first image fusion module 351 may be configured to perform imagefusion processing on the third arterial phase reference image and thethird venous phase reference image to obtain a second target fusionimage. The arterial phase similarity coefficient analysis module 352 maybe configured to perform similarity coefficient calculation on thesecond target fusion image and the third arterial phase reference imageto obtain a second arterial similarity coefficient. Similarly, thevenous phase similarity coefficient analysis module 353 may beconfigured to perform similarity coefficient calculation on the secondtarget fusion image and the third venous phase reference image to obtaina second venous similarity coefficient. The second image fusion module354 may be configured to perform feature enhancement processing on thethird arterial phase reference image by using the second arterialsimilarity coefficient to obtain a second arterial sub-feature image.Similarly, the second image fusion module 354 may be configured toperform feature enhancement processing on the third venous phasereference image by using the second venous similarity coefficient toobtain a second venous sub-feature image. Then, the second image fusionmodule 354 may be configured to perform image fusion processing on thesecond arterial sub-feature image and the second venous sub-featureimage to obtain the first target image corresponding to the thirdarterial phase hepatic image and the third venous phase hepatic image.As shown in FIG. 8 , the first target image corresponding to the thirdarterial phase hepatic image and the third venous phase hepatic imagemay be output through the target image output node 36. An oblique filledarea in FIG. 8 is the highlighted hepatic tumor area.

As such, the obtained image analysis model may process a multi-modalsegmentation problem and a missing modality problem without anymodification, which improves the processing efficiency. Each model of asingle modality may use the dual-modal information implicitly bylearning from other models. That is, since the parameters of thearterial phase feature extraction sub-model and the venous phase featureextraction sub-model are determined by the dual-modal information, whenanother modality is missing, according to the parameters of the arterialphase feature extraction sub-model or the venous phase featureextraction sub-model a better segmentation result may be obtained. Thatis, by combining the features and commonalities of the modalities,through the cooperation of all specific modal models, a bettermulti-modal segmentation effect may be obtained.

For the description of the same step and the same content of embodimentsof the present disclosure, reference may be made to the descriptions inother embodiments, which is not repeated here.

Embodiments of the present disclosure provide an image processingmethod. The method includes obtaining a first quantity of images thatneed to be analyzed and performing fusion and enhancement processing onthe first quantity of images that need to be analyzed to obtain a firsttarget image. As such, since the first quantity is less than a secondquantity of all inputs of the image analysis model, the fusion andenhancement processing may be performed on the first quantity of imagesthat need to be analyzed with missing modality through the imageanalysis model to obtain the first target image for the analysis object.Thus, a problem that image analysis may not be realized when themodality is missing in the current image processing method may besolved. When the modality is missing, the unified image processingmethod may be still be used to perform analysis, which improves theprocessing efficiency of the image processing method.

Based on the above embodiments, embodiments of the present disclosureprovide an image processing apparatus 4. As shown in FIG. 9 , the imageprocessing apparatus 4 includes an acquisition unit 41 and a modelprocessing unit 42.

The acquisition unit 41 may be configured to obtain a first quantity ofimages that need to be analyzed. Each image that needs to be analyzedmay correspond to a different target modality of the target imagingobject.

The model processing unit 42 may be configured to perform fusion andenhancement processing on the first quantity of images that need to beanalyzed through the image analysis model to obtain the first targetimage. The first target image may be used to enhance the display of thedistribution area of the analysis object of the first quantity of imagesthat need to be analyzed. The analysis object may belong to the imagingobject. The image analysis model may be obtained by training the secondquantity of sample images corresponding to the different samplemodalities. The first quantity may be less than or equal to the secondquantity. The target modal may belong to the sample modal.

Based on the above embodiments, in other embodiments of the presentdisclosure, the model processing unit 42 includes a feature extractionsub-model module.

The feature extraction sub-model module may be configured to, if thefirst quantity is 1, based on the feature extraction sub-modelscorresponding to the first quantity of images that need to be analyzedof different target modalities in the image analysis model, process thecorresponding images that need to be analyzed to obtain the first targetimage. The feature extraction sub-model corresponding to each image thatneeds to be analyzed may represent an association relationship betweenthe images that need to be analyzed of the second quantity of differentsample modalities.

Based on the above embodiments, in other embodiments of the presentdisclosure, the model processing unit 42 includes a feature extractionsub-model module and a modal perception sub-model module.

The feature extraction sub-model module may be configured to, if thefirst quantity is greater than or equal to 2, and less than or equal tothe second quantity, through the feature extraction sub-modelcorresponding to the first quantity of images that need to be analyzedof different target modalities in the image analysis model, process thecorresponding images that need to be analyzed to obtain the firstquantity of reference images. The feature extraction sub-modelcorresponding to each image that needs to be analyzed may represent theassociation relationship between the images that need to be analyzed tobe analyzed of the second quantity of different sample modalities.

The modal perception sub-model module may be configured to perform imageprocessing on the first quantity of reference images through the modalperception sub-model of the image analysis model to obtain the firsttarget image.

Based on the above embodiments, in other embodiments of the presentdisclosure, the modal perception sub-model may be configured to performimage fusion processing on reference images of at least two differentmodalities. In some other embodiments, the modal perception sub-modelmay be configured to perform feature enhancement processing on referenceimages of at least two different sample modalities to perform imagefusion processing.

Based on the above embodiments, in other embodiments of the presentdisclosure, after the modal perception sub-model is configured toperform the feature enhancement processing on the reference images ofthe at least two different sample modalities, the modal perceptionsub-model may be configured to perform the image fusion processing. Themodal perception sub-model may be configured to perform image fusionprocessing on the first quantity of reference images to obtain thetarget fusion image, determine the similarity coefficient between eachreference image and the target fusion image to obtain the first quantityof similarity coefficients, and perform the feature enhancementprocessing on the first quantity of similarity coefficients and thefirst quantity of reference images to obtain the first target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, the modal perception sub-model may be configured to performthe feature enhancement processing on the first quantity of similaritycoefficients and the first quantity of reference images to obtain thefirst target image. The modal perception sub-model may implement theabove process by performing the feature enhancement processing on eachreference image using the corresponding similarity coefficient to obtainthe first quantity of sub-feature images and performing the image fusionprocessing on the first quantity of sub-feature images to obtain thefirst target image.

Based on the above embodiments, in other embodiments of the presentdisclosure, the image processing apparatus further includes adetermination unit and a model training unit.

The acquisition unit may be further configured to obtain a thirdquantity of groups of sample images and a third quantity of markedpositions for the analysis object in the third quantity of groups ofsample images and of marked positions for the analysis object. Eachgroup of sample images may include a second quantity of sample imagescorresponding to different sample modalities.

The determination unit may be configured to determine the image modelthat needs to be trained.

The model training unit may be configured to perform model training onthe image model that needs to be trained using the third quantity ofgroups of sample images and the third quantity of marked positions toobtain the image analysis model.

Based on the above embodiments, in other embodiments of the presentdisclosure, the model training unit may be configured to, through theimage model that needs to be trained, perform fusion and enhancementprocessing on the area where the analysis object is located in the thirdquantity of groups of sample images to obtain a third quantity of secondtarget images and perform the model training on the image model thatneeds to be trained based on the third quantity of groups of sampleimages, the third quantity of second target images, and the thirdquantity of marked positions.

Based on the above embodiments, in other embodiments of the presentdisclosure, the model training unit may be configured to perform themodel training on the image model that needs to be trained based on thethird quantity of groups of sample images, the third quantity of secondtarget images, and the third quantity of marked positions to obtain theimage analysis model. The model training unit may be configured toimplement the above process by the following steps.

The loss value between each sample image in the target group sampleimages and the corresponding marked position may be determined to obtainthe first loss value corresponding to the target group sample images.The first loss value corresponding to the target group sample images mayinclude a second quantity of loss values.

The loss value between the second target image corresponding to thetarget group sample images and the corresponding marked position may bedetermined to obtain the second loss value corresponding to the targetgroup sample images.

The first loss value corresponding to the target group sample images andthe second loss value corresponding to the target group sample imagesmay be reversely transferred in the image model that needs to be trainedto continuously train the parameters of the image model that needs to betrained to obtain the image analysis model.

For an information exchange process and description between the unitsand modules of embodiments of the present disclosure, reference may bemade to the interaction process in the image processing method shown inFIGS. 1 to 4 , which is not repeated here.

Embodiments of the present disclosure provide an image processingapparatus. After obtaining the first quantity of images that need to beanalyzed, the image processing apparatus may be configured to performfusion and enhancement processing on the first quantity of images thatneed to be analyzed through the image analysis model to obtain the firsttarget image. As such, since the first quantity may be less than thesecond quantity of all inputs of the image analysis model, the imageprocessing apparatus may be configured to perform fusion and enhancementprocessing on the first quantity of images that need to be analyzed withthe missing modalities through the image analysis model to obtain thefirst target for the analysis object. Thus, the problem that the currentimage processing method cannot analyze the image when the modality ismissing may be solved. Therefore, when the modality is missing, theunified image processing method may be still used to perform theanalysis to improve the processing efficiency of the image processingmethod.

Based on the above embodiments, embodiments of the present disclosureprovide an electronic device. The electronic device may be applied tothe image processing methods of embodiments corresponding to FIGS. 1 to4 . As shown in FIG. 10 , the electronic device 5 includes a processor51, a memory 52, and a communication bus 53.

The communication bus 53 may be configured to realize a communicationconnection between the processor 51 and the memory 52.

The processor 51 may be configured to execute an image processingprogram stored in the memory 52 to implement the implementationprocesses of the image processing method of embodiments corresponding toFIGS. 1 to 4 , which is not repeated here.

Based on the above embodiments, embodiments of the present disclosureprovide a computer-readable storage medium, that is a storage medium.The computer-readable storage medium can be applied to the methodsprovided by embodiments corresponding to FIGS. 1 to 4 . Thecomputer-readable storage medium may store one or more programs. The oneor more programs may be executed by one or more processors to implementthe method implementation processes of embodiments corresponding toFIGS. 1 to 4 , which is not repeated here.

Those skilled in the art should understand that embodiments of thepresent disclosure may be provided as a method, a system, or a computerprogram product. Thus, the present disclosure may take the form of ahardware embodiment, a software embodiment, or an embodiment combiningsoftware and hardware aspects. Furthermore, the present disclosure maytake the form of a computer program product implemented on one or morecomputer-usable storage media (including but not limited to diskstorage, optical storage, etc.) having computer-usable program codes.

The present disclosure is described with reference to flowcharts and/orblock diagrams of methods, apparatuses (systems), and computer programproducts of embodiments of the present disclosure. Each process and/orblock in the flowcharts and/or block diagrams and a combination of eachprocess and/or block in the flowcharts and/or block diagrams may beimplemented by the computer program instructions. These computer programinstructions may be provided to a processor of a general-purposecomputer, a special purpose computer, an embedded processor, or anotherprogrammable data processing device to produce a machine such that theinstructions executed by the processor of the computer or anotherprogrammable data processing device may be used to produce an apparatusconfigured to realize a function specified by a flow or flows of theflowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in acomputer-readable memory capable of directing a computer or anotherprogrammable data processing apparatus to function in a particularmanner. Thus, the instructions stored in the computer-readable memorygenerate an article comprising an instruction device. The instructiondevice implements the functions specified in the flow or flows of theflowcharts and/or the block or blocks of the block diagrams.

These computer program instructions may also be loaded on a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or the otherprogrammable device to produce a computer-implemented process. Thus, theinstructions provide steps for implementing the function specified inthe flow or flows of the flowcharts and/or the block or blocks of theblock diagrams.

The present specification only describes some embodiments of the presentdisclosure and is not intended to limit the scope of the presentdisclosure.

What is claimed is:
 1. An image processing method comprising: obtaininga first quantity of to-be-analyzed images, each to-be-analyzed imagecorresponding to a different target modality of a target imaging object;performing fusion and enhancement processing on the first quantity ofto-be-analyzed images through an image analysis model to obtain a firsttarget image, the first target image being used to enhance display of adistribution area of an analysis object of the first quantity ofto-be-analyzed images, the analysis object belonging to the imagingobject, the image analysis model being obtained by training a secondquantity of sample images corresponding to different sample modalities,the first quantity being less than or equal to the second quantity, andthe target modality belonging to the sample modalities.
 2. The methodaccording to claim 1, wherein performing the fusion and enhancementprocessing on the first quantity of to-be-analyzed images through theimage analysis model to obtain the first target image includes: inresponse to the first quantity being 1, processing the correspondingto-be-analyzed images based on feature extraction sub-models to obtainthe first target image, the feature extraction sub-models correspondingto the first quantity of to-be-analyzed images of different targetmodalities in the image analysis model, a feature extraction sub-modelcorresponding to each to-be-analyzed image representing an associationrelationship between a second quantity of to-be-analyzed images ofdifferent sample modalities.
 3. The method according to claim 1, whereinperforming the fusion and enhancement processing on the first quantityof to-be-analyzed images through the image analysis model to obtain thefirst target image includes: in response to the first quantity beinggreater than or equal to 2 and less than or equal to the secondquantity, processing the corresponding to-be-analyzed images using thefeature extraction sub-models to obtain a first quantity of referenceimages, the feature extraction sub-models corresponding to the firstquantity of to-be-analyzed images of different target modalities in theimage analysis model, the feature extraction sub-model corresponding toeach to-be-analyzed image representing an association relationshipbetween a second quantity of to-be-analyzed images of different samplemodalities; and processing the first quantity of reference imagesthrough a modal perception sub-model of the image analysis model toobtain the first target image.
 4. The method according to claim 3,wherein the modal perception sub-model is configured to: perform imagefusion processing on reference images of at least two differentmodalities; or perform image fusion processing after performing featureenhancement processing on the reference images of the at least twodifferent modalities.
 5. The method according to claim 4, when the modalperception sub-model is configured to perform image fusion processingafter performing feature enhancement processing on the reference imagesof the at least two different modalities, performing image processing onthe first quantity of reference images through the modal perceptionsub-model of the image analysis model to obtain the first target imageincludes: performing image fusion processing on the first quantity ofreference images by using the modal perception sub-model to obtain atarget fusion image; determining a similarity coefficient between eachreference image of the reference images and the target fusion image byusing the modal perception sub-model to obtain a first quantity of thesimilarity coefficients; and performing feature enhancement processingon the first quantity of similarity coefficients and the first quantityof reference images through the modal perception sub-model to obtain thefirst target image.
 6. The method according to claim 5, whereinperforming feature enhancement processing on the first quantity ofsimilarity coefficients and the first quantity of reference imagesthrough the modal perception sub-model to obtain the first target imageincludes: performing feature enhancement processing on each referenceimage of the reference images using a corresponding similaritycoefficient through the modal perception sub-model to obtain a firstquantity of sub-feature images; and performing image fusion processingon the first quantity of sub-feature images through the modal perceptionsub-model to obtain the first target image.
 7. The method according toclaim 1, further comprising: obtaining a third quantity of groups ofsample images and a third quantity of marked positions for the analysisobject in the third quantity of groups of sample images, each group ofsample images including the second quantity of sample imagescorresponding to the different sample modalities; determining an imagemodel; and performing model training on the image model using the thirdquantity of groups of sample images and the third quantity of markedpositions to obtain the image analysis model.
 8. The method according toclaim 7, wherein performing the model training on the image model usingthe third quantity of groups of sample images and the third quantity ofmarked positions to obtain the image analysis model includes: throughthe image model, performing fusion and enhancement processing on an areawhere the analysis object is located in the third quantity of groups ofsample images to obtain a third quantity of second target images; andbased on the third quantity of groups of sample images, the thirdquantity of second target images, and the third quantity of markedpositions, performing model training on the image model to obtain theimage analysis model.
 9. The method of claim 8, wherein based on thethird quantity of groups of sample images, the third quantity of secondtarget images, and the third quantity of marked positions, performingthe model training on the image model to obtain the image analysis modelincludes: determining a loss value between each sample image of targetgroup sample images and a corresponding marked position to obtain afirst loss value corresponding to the target group sample images, thefirst loss value corresponding to the target sample images including asecond quantity of loss values; determining a loss value between asecond target image corresponding to the target group sample images anda corresponding marked position to obtain a second loss valuecorresponding to the target group sample images; and transferring thefirst loss value corresponding to the target group sample images and thesecond loss value corresponding to the target group sample imagesreversely in the image model to continuously train parameters of theimage model to obtain the image analysis model.
 10. An image processingdevice comprising: an acquisition unit, configured to obtain a firstquantity of to-be-analyzed images, each to-be-analyzed imagecorresponding to a different target modality of a target imaging object;and a model processing unit, configured to perform fusion andenhancement processing on the first quantity of to-be-analyzed imagesthrough an image analysis model to obtain a first target image, thefirst target image being used to enhance display of a distribution areaof an analysis object of the first quantity of to-be-analyzed images,the analysis object belonging to the imaging object, the image analysismodel being obtained by training a second quantity of sample imagescorresponding to different sample modalities, the first quantity beingless than or equal to the second quantity, and the target modalitybelonging to the sample modalities.
 11. The device according to claim 1,wherein the model processing unit is further configured to: in responseto the first quantity being 1, process the corresponding to-be-analyzedimages based on feature extraction sub-models to obtain the first targetimage, the feature extraction sub-models corresponding to the firstquantity of to-be-analyzed the images of different target modalities inthe image analysis model, a feature extraction sub-model correspondingto each to-be-analyzed image representing an association relationshipbetween a second quantity of to-be-analyzed images of different samplemodalities.
 12. The device according to claim 10, wherein the modelprocessing unit is further configured to: in response to the firstquantity being greater than or equal to 2 and less than or equal to thesecond quantity, process the corresponding to-be-analyzed images usingthe feature extraction sub-models to obtain a first quantity ofreference images, the feature extraction sub-models corresponding to thefirst quantity of to-be-analyzed images of different target modalitiesin the image analysis model, the feature extraction sub-modelcorresponding to each to-be-analyzed image representing an associationrelationship between a second quantity of to-be-analyzed images ofdifferent sample modalities; and process the first quantity of referenceimages through a modal perception sub-model of the image analysis modelto obtain the first target image.
 13. The device according to claim 12,wherein the modal perception sub-model is configured to: perform imagefusion processing on reference images of at least two differentmodalities; or perform image fusion processing after performing featureenhancement processing on the reference images of the at least twodifferent modalities.
 14. The device according to claim 13, when themodal perception sub-model is configured to perform image fusionprocessing after performing feature enhancement processing on thereference images of the at least two different modalities, the modelprocessing unit is further configured to: perform image fusionprocessing on the first quantity of reference images by using the modalperception sub-model to obtain a target fusion image; determine asimilarity coefficient between each reference image of the referenceimages and the target fusion image by using the modal perceptionsub-model to obtain a first quantity of the similarity coefficients; andperform feature enhancement processing on the first quantity ofsimilarity coefficients and the first quantity of reference imagesthrough the modal perception sub-model to obtain the first target image.15. The device according to claim 14, wherein the model processing unitis further configured to: perform feature enhancement processing on eachreference image of the reference images using a corresponding similaritycoefficient through the modal perception sub-model to obtain a firstquantity of sub-feature images; and perform image fusion processing onthe first quantity of sub-feature images through the modal perceptionsub-model to obtain the first target image.
 16. The device according toclaim 10, wherein the acquisition unit is further configured to obtain athird quantity of groups of sample images and a third quantity of markedpositions for the analysis object in the third quantity of groups ofsample images, each group of sample images including the second quantityof sample images corresponding to the different sample modalities;further comprising: a determination unit, configured to determine animage model; and a model training unit, configured to perform modeltraining on the image model using the third quantity of groups of sampleimages and the third quantity of marked positions to obtain the imageanalysis model.
 17. The device according to claim 16, wherein the modeltraining unit is further configured to: through the image model, performfusion and enhancement processing on an area where the analysis objectis located in the third quantity of groups of sample images to obtain athird quantity of second target images; and based on the third quantityof groups of sample images, the third quantity of second target images,and the third quantity of marked positions, perform model training onthe image model to obtain the image analysis model.
 18. The device ofclaim 17, wherein the model training unit is further configured to:determine a loss value between each sample image of target group sampleimages and a corresponding marked position to obtain a first loss valuecorresponding to the target group sample images, the first loss valuecorresponding to the target sample images including a second quantity ofloss values; determine a loss value between a second target imagecorresponding to the target group sample images and a correspondingmarked position to obtain a second loss value corresponding to thetarget group sample images; and transfer the first loss valuecorresponding to the target group sample images and the second lossvalue corresponding to the target group sample images reversely in theimage model to continuously train parameters of the image model toobtain the image analysis model.
 19. A computer-readable storage mediumstoring a program that, when executed by a processor, causes theprocessor to: obtain a first quantity of to-be-analyzed images, eachto-be-analyzed image corresponding to a different target modality of atarget imaging object; perform fusion and enhancement processing on thefirst quantity of to-be-analyzed images through an image analysis modelto obtain a first target image, the first target image being used toenhance display of a distribution area of an analysis object of thefirst quantity of to-be-analyzed images, the analysis object belongingto the imaging object, the image analysis model being obtained bytraining a second quantity of sample images corresponding to differentsample modalities, the first quantity being less than or equal to thesecond quantity, and the target modality belonging to the samplemodalities.
 20. The computer-readable storage medium according to claim19, wherein the processor is further caused to: in response to the firstquantity being 1, process the corresponding to-be-analyzed images basedon feature extraction sub-models to obtain the first target image, thefeature extraction sub-models corresponding to the first quantity ofto-be-analyzed images of different target modalities in the imageanalysis model, a feature extraction sub-model corresponding to eachto-be-analyzed image representing an association relationship betweenthe second quantity of to-be-analyzed images of the different samplemodalities.